Result for 37C4F647695058B7E427DB29895FBF9B8075B019

Query result

Key Value
FileName./usr/lib64/R/library/Numero/Meta/package.rds
FileSize1209
MD527D0A6AE3441BC3242D9110DFE6D3530
SHA-137C4F647695058B7E427DB29895FBF9B8075B019
SHA-256F3B02847FD3910C48ECAA076C78D1BB1E226C33571DD847FC0CF87B3D0538C24
SSDEEP24:X30Y2xhTLFBJXnB5lAKTOcGTZAjvWPaNHe7s8zZbKTh4Av4ec00l:XEZnTLbnAKeTZAD8MwUTh4Aijl
TLSHT12721E430541249DCC8A62EA9B7CA513C9098C10AC80FE0B5ED6AC378A225C32DB1C0AE
hashlookup:parent-total1
hashlookup:trust55

Network graph view

Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD534E954324378B2C3F57CEBD3B2F829D4
PackageArchx86_64
PackageDescriptionHigh-dimensional datasets that do not exhibit a clear intrinsic clustered structure pose a challenge to conventional clustering algorithms. For this reason, we developed an unsupervised framework that helps scientists to better subgroup their datasets based on visual cues, please see Gao S, Mutter S, Casey A, Makinen V-P (2019) Numero: a statistical framework to define multivariable subgroups in complex population-based datasets, Int J Epidemiology, 48:369-37, <doi:10.1093/ije/dyy113>. The framework includes the necessary functions to construct a self-organizing map of the data, to evaluate the statistical significance of the observed data patterns, and to visualize the results.
PackageNameR-Numero
PackageReleaselp152.1.2
PackageVersion1.8.4
SHA-196BE57AE4E1C8047578E8B593441CC4B2CE846C4
SHA-2569A511B3B4AF02A143C97AE783AD0A5FD1ADEFD166BFB2FADC04BD5AFEDAC0268